Basic Machine Learning Algorithms

CAD Phase 1: Linear Regression on IMD Temperature Analysis

• Machine Learning Algorithm Used: Linear Regression
• DataSet Used: IMD (Indian Meteorological Department) Temperature Analysis
• Visualization Techniques Used: Matplotlib Scatter
• Description: Utilized Linear Regression to analyze temperature data from IMD. Matplotlib Scatter plots were employed for insightful visualizations, providing a foundation for further climate and weather-related research.

CAD Phase 2: SVM and Bagging Ensemble on Pima Diabetes Dataset

• Machine Learning Algorithms Used: Support Vector Machine (SVM) and Bagging Ensemble
• DataSet Used: Pima Diabetes Dataset
• Visualization Techniques Used: Matplotlib Histogram
• Description: Applied SVM and Bagging Ensemble to analyze and predict diabetes using the Pima Diabetes Dataset. Matplotlib Histograms were used to understand feature distributions, enhancing our diabetes analysis and prediction capabilities.

CAD Phase 3: Random Forest Regressor on IBM DB2 Dataset

• Machine Learning Algorithm Used: Random Forest Regressor
• Database Used: IBM DB2 Dataset Created Using SQL Commands
• Description: Employed Random Forest Regressor on a dataset created from an IBM DB2 database using SQL commands. The algorithm was used for regression tasks, providing valuable insights and predictions based on the database.

CAD Phase 4: Multiple Regression on USA House Price Dataset

• Machine Learning Algorithm Used: Multiple Regression
• DataSet Used: USA House Price Dataset
• Visualization Techniques Used: Matplotlib Scatter
• Description: Applied Multiple Regression on the USA House Price Dataset to model relationships between multiple variables and house prices. Matplotlib Scatter plots were utilized for visualizing these relationships, contributing valuable insights for decision-making in the real estate market.

How to Use

1. Clone this repository:

   git clone https://github.com/AHBRIJESH/IBM_Projects.git
   cd IBM_Projects.git

2. Explore individual CAD phases by navigating to respective directories (e.g., cad-phase-1, cad-phase-2, etc.).

3. Review Jupyter Notebooks and code files for each phase to understand implementations and visualizations.

4. Contribute by opening issues, providing suggestions, or submitting pull requests to enhance project functionality or documentation.

Thank you for exploring the IBM Project Repository! Contributions and feedback are highly appreciated.